Model predictive control design for linear parameter varying systems: A survey
Motivated by the fact that many nonlinear plants can be represented through Linear
Parameter Varying (LPV) embedding, and being this framework very popular for control …
Parameter Varying (LPV) embedding, and being this framework very popular for control …
Fault detection for systems with model uncertainty and disturbance via coprime factorization and gap metric
The fault detection (FD) problem for systems with both model uncertainty and external
disturbance is investigated in this article. First, the mathematical models of systems with …
disturbance is investigated in this article. First, the mathematical models of systems with …
Auto-switch Gaussian process regression-based probabilistic soft sensors for industrial multigrade processes with transitions
Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial
processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is …
processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is …
Multiple model predictive control for large envelope flight of hypersonic vehicle systems
Considering the strong nonlinearity, wide flight envelope and hard constraints of hypersonic
vehicle (HV), we present a multiple model predictive control (MMPC) strategy for the control …
vehicle (HV), we present a multiple model predictive control (MMPC) strategy for the control …
Economic model predictive control of nonlinear process systems using empirical models
Economic model predictive control (EMPC) is a feedback control technique that attempts to
tightly integrate economic optimization and feedback control since it is a predictive control …
tightly integrate economic optimization and feedback control since it is a predictive control …
A novel semi-active control strategy based on the quantitative feedback theory for a vehicle suspension system with magneto-rheological damper saturation
This paper presents a robust controller for a semi-active suspension system with actuator
saturation. It addresses the vehicle vibration attenuation problem under two cases:(i) without …
saturation. It addresses the vehicle vibration attenuation problem under two cases:(i) without …
Wind turbine torque oscillation reduction using soft switching multiple model predictive control based on the gap metric and Kalman filter estimator
A new multiple model predictive control (MMPC) is reported to regulate the output power of
the National Renewable Energy Laboratory (NREL) 1.5 MW baseline wind turbine (WT). The …
the National Renewable Energy Laboratory (NREL) 1.5 MW baseline wind turbine (WT). The …
A stabilizing sub-optimal model predictive control for quasi-linear parameter varying systems
Quasi-Linear Parameter Varying (Q-LPV) systems are often obtained as convex
combinations of LTI models and have been widely applied for the control of nonlinear …
combinations of LTI models and have been widely applied for the control of nonlinear …
A novel dynamic just-in-time learning framework for modeling of batch processes
T Joshi, V Goyal, H Kodamana - Industrial & Engineering …, 2020 - ACS Publications
A novel dynamic just-in-time (JIT) learning framework is proposed in this paper for the data
driven modeling of batch process. In the proposed JIT framework, we employ a searching …
driven modeling of batch process. In the proposed JIT framework, we employ a searching …
Decentralized multi-agent control of a three-tank hybrid system based on twin delayed deep deterministic policy gradient reinforcement learning algorithm
In this study, a reinforcement learning (RL) method called twin delayed deep deterministic
policy gradient (TD3) is used to tune the parameters of the proportional-integral (PI) …
policy gradient (TD3) is used to tune the parameters of the proportional-integral (PI) …